Pan-sharpening aims to generate high-resolution multi-spectral (MS) images by fusing PAN
images and low-resolution MS images. Despite its great advances, most existing pan …

Pan-sharpening aims to integrate the complementary information of texture-rich PAN images
and multi-spectral (MS) images to produce the texture-rich MS images. Despite the …

In this article, we propose an efficient convolutional long short-term memory (ConvLSTM)
network with dual-direction transfer for pan-sharpening, termed D2TNet. We design a …

Anomaly detection of hyperspectral data has been gaining particular attention for its ability in
detecting targets in an unsupervised manner. Autoencoder (AE), together with its variants …

As recognized, the ground-truth multispectral (MS) images possess the complementary
information (eg, high-frequency components) of low-resolution (LR) MS images, which can …

Polarization imaging can retrieve inaccurate objects' 3D shapes with fine textures, whereas
coarse but accurate depths can be provided by binocular stereo vision. To take full …

Pan-sharpening aims to obtain high-resolution multispectral (MS) images for remote sensing
systems and deep learning-based methods have achieved remarkable success. However …

Pansharpening is about fusing a high spatial resolution panchromatic image with a
simultaneously acquired multispectral image with lower spatial resolution. In this paper, we …

Deep-learning-based super-resolution (SR) methods have been extensively studied and
have achieved significant performance with deep convolutional neural networks. However …

It is observed that, in most of the CNN-based pansharpening methods, the multispectral
(MS) images are taken as the ground truth, and the downsampled panchromatic (Pan) and …